Switched networks and multi-state devices are commonly used in various electronic applications. Such networks include, for example, rectifiers, AC/DC converter and DC/AC inverters. The design and optimization of the non-linear switching systems require extensive mathematical modeling. In designing such a process model, it is highly desirable to be able to acquire and to understand the steady-state performance for the network in advance.
For example, the steady-state output ripple voltage is a key parameter in evaluating the performance of a switching inverter. In order to obtain such information scientists and engineers conventionally resort to conventional simulation and modeling software. The conventional models offer a few data points and do not provide a high degree of confidence. Other disadvantages include extensive simulation time, possible model failure and uncertainty about the steady-state of the system.
Because the conventional simulations start from the so-called zero state, passing through a transient initial states can be very time-consuming. Indeed, the model can spend upward of several hours passing through the transient phase from the initial zero state. In addition, conventional simulations rely on differential equations that must converge at the boundary which separates two sequential states. As a result, these simulations often fail because of the complexity attributed to the unpredictable boundary conditions and the complexity of solving multiple differential equations. Finally, the conventional simulations do not detect and cannot predict when the steady state is reached.